With the rapid development of industry and agriculture, the situation of Cd pollution in farmland soil is severe. Cd pollution in farmland will directly or indirectly have adverse effects on soil ecological security, crop growth and human health development. Cd has received extensive attention due to its strong biological toxicity and easy migration and accumulation, and effective management and control of Cd pollution in farmland is urgently needed. At present, the remediation technology of Cd pollution in farmland soil has been gradually developed and enriched, but the summary analysis of the remediation technology is relatively lacking. The characteristics and hazards of farmland soil Cd contamination were analyzed, focusing on the current status and sources of farmland Cd pollution. Based on this analysis, the principles, characteristics, and applicable scopes of remediation technologies for Cd-contaminated farmland soil were summarized. Case studies were used to compare the practical application effects of these remediation technologies. Additionally, the advantages, disadvantages, and limitations of various remediation approaches were examined, providing theoretical references for the prevention and remediation of farmland soil Cd pollution and promoting high-quality development of agriculture.

In order to predict perforation velocity of SCS (steel-concrete-steel) slab against missile impact and obtain influence sequence of structural factors of SCS slab on the perforation velocity, a dimensionless equation of the perforation velocity was established and a prediction model was obtained based on dimensional analysis and artificial neural network. Orthogonal experiment design was used to determine finite element calculation, and the influence degree of 7 factors of SCS slab on perforation velocity was quantitatively evaluated by variance analysis. The deviation between the predicted perforation velocity and the actual value is less than 12%, and the quantitative evaluation results of variance analysis show that the thickness of steel plate has a largest effect, followed by the distance of tie bar and the thickness of concrete, the yield strength of steel plate, the yield strength of tie bar and the diameter of tie bar have a smaller effect, and the concrete compressive strength has a smallest effect. The established model solves the prediction problem of perforation velocity from a new perspective with a good prediction effect, which can effectively evaluate the ability of SCS slab against perforation failure of missile impact, and the order of the factors is beneficial to the optimal design of SCS slab resisting perforation from missile impact.

As an important aluminum industrial base in China, the bauxite concentration area in central Guizhou Province is hosted within the Lower Carboniferous Jiujialu Formation, and the deposit type belongs to the sedimentary bauxite. The Caijiaba bauxite deposit is a newly discovered bauxite deposit during the fine investigation of bauxite in central Guizhou Province in recent years. The paleoclimatic conditions, sedimentary environment and metallogenic provenance related to this bauxite deposit in the metallogenic process were studied. The results show that the upper bauxitic claystone of the Al-bearing rock series is mainly pelitomorphic texture and is mainly composed of kaolinite and illite. The middle bauxite ore is mainly clastic and cryptocrystalline textures, followed by a small number of ooidal texture, which is mainly composed of diaspore and illite. The lower ferruginous rock is mainly cryptocrystalline texture and is mainly composed of hematite. The Al-bearing rock series of the Caijiaba bauxite deposit is mainly formed in the continental environment, and the upper bauxitic claystone and middle bauxite ore layers are mainly formed in a relatively oxidized environment, while the lower ferruginous rock layer mainly shows a relatively reduced environment. Meanwhile, the lower ferruginous rock and middle bauxite ore layers are mainly formed in hot and humid paleoclimatic conditions, while the upper bauxitic claystone layer is the product of warm and humid climatic conditions. Research on the sources of ore-forming materials indicates that the Al-bearing rock series of the Caijiaba bauxite deposit is mainly derived from the dolomite and interbedded gray-green claystone of the Lower Cambrian Qingxudong Formation.The results are of great significance in guiding the exploration and prospecting of bauxite deposits in this area.

In order to apply remote sensing technology to uranium exploration in the Mouding area of Yunnan Province, based on ASTER (advanced spaceborne thermal emission and reflection radiometer) remote sensing data, the interference removal and PCA (principal component analysis) method was used to extract the Al-OH, Mg-OH, {\mathrm{CO}}_3^{2-} and iron-stained alteration information in the study area. The lineaments in the study area were automatically extracted by PCA and LINE model in PCI Geomatica software, and the density map of lineaments was created. Finally, combined with geological data, the relationship between uranium mineralization and alteration and linear structures in the study area was analyzed, and a favorable mineralization area was delineated. This study can provide some ideas for subsequent exploration in the area, and also provide some reference for remote sensing technology in mineral exploration in vegetation-covered areas.

In order to analyze the effect of maximum window area and multi-resolution DEM (digital elevation model) on the extraction of optimal area for relief amplitude, explore feasible solutions to quantitatively analyze the optimal area, and classify landforms based on the best results of various types data. Based on three DEM data obtained, including ALOS (advanced land observing satellite), ASTER GDEM.V2 (Version 2 of the advanced spaceborne thermal emission and reflection radiometer global digital elevation model) and SRTM3 (3 arc-seconds shuttle radar topography mission), the influence mechanism was statistically analyzed by calling the Arcpy module and using the mean variation point method, respectively, with the maximum window area and the resolution of the DEM as a single variable. In order to determine the appropriate maximum window area, the traversal method to correlate the relief amplitude classification results with the DEM data were proposed, to determine the optimal area for different data according to the maximum correlation coefficient, and to arbitrate the elevation classification data to obtain the geomorphic distribution map. The results show as follows. The optimal area increases in a stepwise manner with the increase of the maximum window area. There is a negative correlation between the DEM resolution and the optimal area in the same area range, that is, the corresponding area decreases sequentially with the increase of resolution. Based on the correlation analysis, the maximum correlation coefficient of ALOS DEM is 0.785 0, which corresponds to the optimal area of 0.21 km². ASTER GDEM.V2 DEM is 0.764, with an area of 0.32 km², and SRTM3 DEM is 0.782, with an area of 0.40 km². The geomorphological classification maps corresponding to the optimal areas of the three data were obtained, and by comparing them with the spatial distribution of China's 1∶1 million geomorphological types, it is concluded that the distribution of geomorphological features of the experimental data is more reasonable, and the boundaries are more clear. It is concluded that the maximum window area has a greater influence on the optimal area compared to the DEM resolution, and in order to solve the influence of the maximum window area, the correlation analysis can provide a theoretical basis for quantitatively determining the optimal area of relief amplitude.

In order to reveal the association patterns between ferroptosis-related diseases and genes and predict potential Disease-Gene associations, ferroptosis-related research literature was analyzed to extract disease and gene entities, and a disease-gene complex network was constructed. The network's basic characteristics were further analyzed, and the Apriori algorithm was applied to extract strong disease-gene association rules. Link prediction technology was used to identify potential disease-gene associations. The results show as follows. ferroptosis plays a critical role in lethal diseases such as hepatocellular carcinoma, adenocarcinoma, breast cancer, and colorectal cancer. The genes such as GPX4 and ROS play key roles in cell survival or death through the regulation of iron homeostasis, oxidative stress, and lipid peroxidation. GPX4 and ROS are significantly associated with various diseases. The link prediction method revealed potential target genes for adenocarcinoma, lung cancer, colorectal cancer, and breast cancer, and preliminary validation of some predicted results was conducted through literature review.It is concluded that the research methodology employed in this study is both feasible and effective. The findings offer valuable references and suggest future directions for research on the prevention and treatment of ferroptosis-related diseases.

In order to study the relationship between VCI (vascular cognitive impairment) and intracranial and extracranial large artery stenosis, cerebral white matter lesions and brain atrophy. By consecutively enrolling 105 patients with VCI, divided into mild group (n=77) and severe group (n=28), and at the same time selecting patients with normal cognition as the control group (n=71). comparing the differences in cerebrovascular disease risk factors, cerebral white matter lesions, ischemic cerebral infarction, and cerebral atrophy among the 3 groups, and analysing the correlation between the degree of stenosis of the intracranial and extracranial large arteries and VCI. The results show that the differences in the history of ischaemic stroke and the proportion of ≥2 lacunae were statistically significant among the 3 groups (P<0.001).The differences in cerebral white matter high signal, paraventricular white matter, deep white matter Fazekas score, and whole-brain cortical atrophy GCA grading were statistically significant among the 3 groups (P<0.001). In the multivariate ordered logistic regression analysis model, it was found that internal carotid artery segment C1, internal carotid segment C2~C7, and the degree of middle cerebral artery stenosis are the main influencing factors for the severity of VCI. The degree of stenosis of internal carotid artery C1 segment and internal carotid C2~C7 segment is positively correlated with the severity of VCI patients to a low degree, whereas the degree of stenosis of the middle cerebral artery, cerebral white matter lesions, and cerebral atrophy grading are positively correlated with the severity of VCI patients to a moderate degree. It is evident that with increasing cardiovascular risk factors, history of ischaemic stroke and degree of stenosis of the internal carotid and middle cerebral arteries, the risk of VCI in the subjects increased significantly. It suggests that the condition of intracranial and extracranial large arterial lesions can be used as one of the indicators for the detection of VCI, and that there are certain feasible therapeutic directions.

It is a new problem for the prevention and control of non-point source pollution to adsorb pollutants by colloids and assist them to quickly migrate from soil to water during the rainfall-runoff process. Southwest Guizhou Province is one of the important ecological barrier areas in the Pearl River Basin, but some areas sow corn in the middle and late April and enter the rainy season in May. The destruction of soil structure, the significant increase of precipitation and the typical karst landforms in the region lead to high water environmental risks in sloping farmland areas. Undisturbed soil samples are collected from newly ploughed yellow soil slope farmland in karst area. These samples undergo simulated rainfall infiltration experiments. The purpose is to investigate the dynamic release rule of colloids under different rainfall intensities, including the change characteristics of colloid concentration, particle size distribution and its content level with the increase of accumulated rainfall. The results show as follows. The colloid concentration increased with the increase of rainfall intensity and cumulative infiltration, and the study further revealed that the difference of released soil colloid concentration under three rainfall intensities showed three different stages: the colloid concentration is not significantly different when the rainfall intensity is 25 mm/h and 40 mm/h in the 0~100 mm stage, but is significantly higher than 10 mm/h. The colloid concentration in the 100~250 mm stage is quite different under three rainfall intensities. When the accumulated rainfall is more than 250 mm, the difference between them is very small. When the rainfall intensity is 40 mm/h, the characteristic statistics of colloidal particle size show obvious two-stage and sudden drop characteristics. At first, the average particle size of effluent increases with the increase of rainfall intensity, and then decreases slightly with the further increase of rainfall. When the rainfall intensity is 25 mm/h and 10 mm/h, it presents a gradual change characteristic. The change of colloid content with different particle sizes shows a trend of stability, increase and decrease respectively. The research innovatively reveals the stage characteristics of the outflow concentration difference of soil colloids at different flow rates, and quantifies the change trend of colloids with different particle sizes with the cumulative infiltration from the meso-scale, which will provide a reference for further evaluating the regional water environmental risk and driving mechanism in this period.

To compare and analyze the differences in pore structure characteristics of coal from Huainan and Huaibei, this study focuses on No. 13 coal from Liuzhuang Mine in Huainan mining area and No. 7 coal from Qidong Mine in Huaibei mining area. Using mercury intrusion porosimetry and low-temperature nitrogen adsorption methods, the pore structures were analyzed, and fractal theory was applied to study the fractal characteristics of the pore structures. The differences in pore structures between Huainan and Huaibei coals were compared and analyzed. The mercury intrusion results show that the total pore volume and specific surface area of No.13 coal from Liuzhuang are 3.488 mL/g and 0.02 m²/g, respectively, while those of No.7 coal from Qidong was 4.926 mL/g and 0.027 m²/g, respectively, with Qidong coal having a larger pore volume. Both coals show the largest pore volume in macropores and the smallest in mesopores. Low-temperature nitrogen adsorption results indicate that the specific surface area ratio of micropores in No.13 coal from Liuzhuang is 76.25%, showing the most developed micropores. The specific surface area ratio of small pores in No.7 coal from Qidong is 79.79%, showing the most developed small pores. Fractal analysis demonstrate that both coals exhibited fractal characteristics. From mercury intrusion data, the fractal dimensions of pores larger than 100 nm for No.13 coal from Liuzhuang and No.7 coal from Qidong are 2.805 6 and 2.756 7, respectively. From low-temperature nitrogen adsorption data, the fractal dimensions of pores smaller than 100 nm for No.13 coal from Liuzhuang and No.7 coal from Qidong are 2.727 5 and 2.037 2, respectively, all within the range of 2 to 3. This indicates that the pore structure of No.13 coal from Liuzhuang is more complex and heterogeneous, especially in the range of small pores and micropores, where the fractal dimension differences are more significant. This may be related to differences in maceral composition and mineral content, where vitrinite is the main component for pore development in coal and shows a positive correlation with pore fractal dimensions. Exinite and inertinite are not primary components for pore development and show a negative correlation with pore fractal dimensions. Uneven mineral distribution may increase the heterogeneity and complexity of the coal pore structure, thus showing a positive correlation between mineral content and fractal dimensions in the two types of Huainan and Huaibei coal samples.

The influence of hydrothermal fluids on fluid-rock interactions and hydrocarbon generation in basins is of great significance. The mineralogical and geochemical characteristics of hydrothermal activity in the black shale of the Wufeng Formation-Longmaxi Formation in southeastern Sichuan was investigated using advanced techniques such as large field splicing scanning electron microscopy, mineral quantitative analysis, X-ray diffraction, isotopes, and electron probes. The results show that non-metallic minerals such as barium ice feldspar, calcite, apatite, and barite, as well as metal minerals like sphalerite, pyrite, galena, and chalcopyrite, exhibit distinct characteristics of hydrothermal activity. The in-situ Sr isotope ratio of barite ranges from 0.719 76 to 0.723 94, with an average of 0.722 37. The carbonate mineral content is exceptionally high, up to 60%. Based on previous research, mineralogical and geochemical indicators for hydrothermal fluid activity in the Wufeng Formation-Longmaxi Formation of southeastern Sichuan were established, suggesting that hydrothermal fluids have detrimental effects on reservoir space.

Unconventional tight reservoirs are characterized by strong non-homogeneity, low resource abundance, low porosity and low permeability. Compared with onshore unconventional reservoirs, hydraulic fracturing of offshore tight gas reservoirs has greater safety risks greater and more operational difficulties, leading to greater difficulty in balanced reservoir modification. Therefore, deployment of horizontal wells for hydraulic fracturing development in offshore tight gas reservoirs requires precise evaluation of reservoir geomechanical characteristics, analysis of the fracturability of horizontal well sections, and fracturing of well sections with similar geomechanics, which can support precise, balanced and efficient development of offshore gas reservoirs. Taking a tight gas reservoir in offshore as the research object, in this study, an engineering fracturability analysis method based on brittleness and in-situ stress difference was proposed, and an engineering sweet spot prediction model was established by kernel density estimation method to estimate the probability density function and cumulative probability function of engineering fracturability. Based on the engineering sweet spot prediction, hydraulic fracturing segments were optimized to achieve a differentiated design of segment spacing. Based on the concept of integrated geological and engineering, hydraulic fracturing simulation and production prediction were carried out in this study. The simulation results verified the feasibility of hydraulic fracturing and the reliability of engineering sweet spot prediction in target reservoir, which provides a significant technical support for the optimization of horizontal well fracturing scheme and large-scale beneficial development of offshore tight gas reservoirs.

The injection head is the key equipment to drive the coiled tubing in the coiled tubing operation machine. A fault occurred during the operation of a LG450 injection head, and it was found that the chain drive system was seriously damaged after disassembly. The macro and micro morphology analysis of the chain drive system shows that there are arc-shaped scratches on the outer side of the gear teeth and inclined scratches on the tooth surface. The chemical composition test, hardness test and metallographic test were carried out on the sprocket teeth, and the results showed that the material met the process requirements. The finite element simulation of the meshing process of the sprocket and the chain is carried out. The results show that the contact pressure distribution is consistent with the friction marks on the tooth surface and side of the failed sprocket. It is revealed that the failure reason of the chain drive system of the injection head is the spatial intersection of the chain roller and the sprocket axis, and the abnormal meshing of the roller and the gear teeth. This study shows that the motion state of the sprocket chain will have an important impact on the safety of the injection head, which is of great significance to guide the design and processing of the injection head.

The flooding cap is the key equipment for the pre-commissioning of subsea pipeline. Its deep sea installation operation has high risks and strict requirements, which puts forward higher requirements for the safety performance of structural strength. In addition to bearing loads during installation, the flooding cap also needs to block huge internal pressure in the pipeline during pressure test. Its structural strength and bearing capacity directly affect the safety and reliability of the whole subsea production system pre-commissioning. The flooding cap used in a 1 500 m deep gas field in the South China Sea Lingshui area was taken as the research object. Based on the relevant standards of DNV and NORSOK, elastoplastic finite element modeling of the flooding cap and key pressure components was carried out to analyze the safety strength requirements under different working conditions. The results show that the maximum Von Mises stress of each component of the flooding cap is less than the allowable stress under lifting conditions, and the high stress is mainly concentrated at the bolt connection. Under the impact condition, the flooding cap simulation can meet the installation speed requirement of 0.5 m/s, and the overall structural strength can meet the relevant standards. Under pressure testing conditions, the pressure capacity of the flooding cap is calculated to be 823 bar, and the measured pressure during the pre-commissioning operation of the Linsgshui gas field in the South China Sea is 268 bar, which is far less than its pressure capacity, satisfying the finite element calculation results. The relevant research results can provide theoretical basis and technical reference for the design and field application of flooding cap.

The key factor to control the shape of salt cavern cavity is to control the depth of the interface between solvent and brine, and the accurate monitoring of the interface is the prerequisite for its control. The current two-phase media interface monitoring technology has the disadvantages of poor real-time performance and low precision. A special algorithm for two-phase media monitoring in cavity well was proposed. Distributed optical fiber was used to collect the temperature data in the vertical direction of the cavity, and the original temperature curves were respectively processed by local weighted regression analysis and Kalman filtering. Through the difference calculation of the treated curve, the region with the most obvious temperature difference was identified, and the interface position of the two-phase medium was preliminatively determined. Then, the temperature curve in the initial location area was processed by convolutional smoothing filtering, the weighted rate of change of the convolutional smoothing filter curve was calculated, the maximum value of the temperature change rate was found, and the specific location of the interface of the two-phase medium was finally determined. The algorithm was used to monitor the interface of two phase media under actual conditions. The experimental results show that the error of the measured data obtained by this system is controlled within 0.3 m compared with that obtained by neutron logging method. Compared with the traditional optical fiber monitoring technology, the proposed algorithm has the advantages of simple operation, high measurement accuracy and high reliability.

Aiming at the problem of determining the effective design range of parameters during the optimization design of planetary gear transmission system, the dynamic sensitivity of two-stage planetary gear transmission system parameters was studied. Taking the two-stage planetary gear transmission system of vehicle as the research object, the natural vibration model of the system was established by Lagrange, the vibration modal characteristics of the two-stage planetary system were summarized, the expression of the vibration energy of the planetary system was deduced, the distribution state and the law of the vibration energy transfer in the same order were studied, and the mode change mechanism of the natural frequency of the system was further explored. The vibration energy transfer essential of the system natural frequency mode transition triggered by parameter change was analyzed. The parameter sensitivity equation of the natural frequency of the system is derived, the influence law of the modal transition phenomenon on the parameter sensitivity was studied, and the dynamic change law of the natural frequency sensitivity with the change of the parameter value was revealed. The method of dividing the parameter sensitivity interval based on the modal transition phenomenon was proposed, which realizes the effective guidance for the selection of the variable value range in the optimization design stage.

To solve the problem of bearing fault and complex sound field environment, taking H7009C full ceramic angular contact ball bearing as the research object, the dynamic analysis model of full ceramic angular contact ball bearing was established, and the error of theoretical calculation and simulation of rolling body was compared to verify the validity of the model. Based on the transient dynamic analysis of the influence of inner ring fault on the bearing dynamic characteristics, the surface SPL (sound pressure level) of bearings caused by different faults was calculated, and the SPL characteristics of high-speed bearings were compared under the action of variable speed and variable load. The results show that the sound pressure level of the faulty inner ring bearing increases with the increase of speed, and increases first and then decreases with the increase of load. When the ratio of the lowest sound pressure level frequency to the vibration frequency is close to 0.75 or the ratio of the highest sound pressure level frequency to the vibration frequency is close to 4, it can be identified as an inner ring fault.

In the manufacturing process of wind turbine blades, gluing is an important part of ensuring the structural strength and tightness of the blades. In view of the low efficiency, uneven gluing, low quality of glue line and glue overflow on the surface of parts caused by the manual sealing and gluing operations commonly used in the gluing process of wind turbine blades, a kind of intelligent rubber shoe for wind turbine blade gluing with servo motor and reducer as driving components was developed, and the gluing process was introduced in detail. ANSYS analysis was carried out on the key components, and after simulation analysis, it was found that the structure of the intelligent rubber shoe could meet expectations. After experiments, it is verified that the shape of the rubber road is full, the two sides are smooth and flat, and the width and thickness of the rubber road meet expectations. The use of rubber shoes can not only reduce the work intensity of workers, but also reduce the use of 10% of the glue amount when gluing, and improve the efficiency and quality of gluing.

Due to the special double salient pole structure of switched reluctance motor, there will be large Torque ripple during operation. In order to reduce the peak current and torque ripple during phase commutation, a novel TSF (torque sharing function) control method is proposed. Firstly, considering the relation between torque and inductance, the exchange was divided into two subintervals by inductance boundary points, and different TSF curves were designed in different intervals. Secondly, with the increase of motor speed and load, the fixed overlap Angle will reduce the efficiency of the motor, and an online overlap Angle optimization control strategy was proposed. Finally, the simulation and experimental results show that compared with the traditional cubic torque distribution function, the torque ripple and current peak value of the proposed method are reduced by 3.6%, 12%, and 1.1 A, 3.2 A respectively at a load of 5 N·m and a speed of 500 r/min and 1 000 r/min respectively. The proposed method can effectively reduce torque ripple and current peak value.

Photovoltaic power generation has an important place in the energy sector. In order to accurately quantify the uncertainty and fluctuation range of PV(photovoltaic) power and to improve the comprehensiveness of interval forecasts, a probabilistic prediction method for PV power intervals based on feature mining with improved TCN-BiGRU was proposed. First, the maximum information coefficient and symbolic transfer entropy causal analysis were utilized to screen the meteorological features, remove redundant information, and construct global horizontal radiation trend features, seasonal features, and weather clustering features to provide more effective information. Subsequently, the TCN-BiGRU model was improved by combining the temporal pattern attention mechanism and quantile regression methods to construct a combined model for interval prediction. Finally, the probabilistic prediction results are generated using the KDE method of empirical bandwidth selection with scatter measure semi-polar optimization. The proposed method is analyzed by real PV plant data, which verifies the high reliability and applicability of the proposed method in PV power interval probability prediction.

In the context of large-scale integration of wind and solar power into the grid, power system dispatch strategies faced unprecedented challenges. The volatility and randomness of wind and photovoltaic power generation significantly impacted system stability and controllability. To accurately characterize the spatiotemporal correlation of wind-solar power output and construct a practically valuable scenario set, a method for generating spatiotemporal correlated scenarios for wind-solar complementary systems was proposed, based on a coupled SGMM (seasonal Gaussian mixture model) and MCopula (mixed Copula function). Initially, the SGMM was constructed to capture the temporal correlation among wind-solar output variables. Then, the mixed Copula function was employed to describe the spatial correlation among variables. Based on the comprehensive modeling of spatiotemporal correlations, a series of uncertainty scenario sets reflecting these characteristics was generated using the Copula conditional distribution function and inverse transform sampling technique. The simulation results confirmed the effectiveness and reliability of the proposed method. The generated scenario sets not only reflected the spatiotemporal correlation characteristics and annual variation trends of wind-solar output but also better matched the historical actual sequences in terms of distance, providing strong decision-making support for power system dispatch. New perspectives and tools were offered for quantifying uncertainties in wind-solar complementary systems, which had profound theoretical and practical significance for optimizing power system dispatch strategies, reducing uncertainty risks, promoting the efficient utilization of renewable energy, and advancing the sustainable development of power systems.

Vegetation fires may lead to tripping and shutdown of overhead transmission lines, thus endangering the safe and stable operation of power grids. In order to study the change of flame inclination of vegetation under the influence of ambient wind, a square fir crib with the size of 1 m×1 m×0.6 m was used as the research object, and a 5 m×5 m×5 m outdoor site with ventilation was set up by Pyrosim software, and simulation research was carried out on 10 free burning cases with the wind speed of 1~10 m/s, and the change of the flame pattern with the size of the wind was analysed. The formula for calculating the flame inclination applicable to this wind speed range was fit. The flame inclination calculation formula was based on the flame inclination of this range, and the flame inclination calculation formula was based on the flame inclination of this range. The change of flame shape with the wind speed was analyzed. The formula for calculating the flame inclination angle applicable to the wind speed range was fitted. And the horizontal offset distance of the flame was obtained according to the change of the flame inclination angle. The results show that the larger the wind speed is, the smaller the change of flame inclination and flame horizontal offset distance are. The results of this study can be used as a reference for the prevention of tripping accidents on transmission lines and the cutting distance of vegetation on both sides of transmission line corridors.

When traditional methods were used to evoked the potentials SSVEP (steady-state visual evoked potentials) EEG(electroencephalogram) signals, the accuracy and sufficiency of feature extraction were insufficient, which affected the recognition accuracy of signals. A novel approach was proposed which based on a CNN (convolutional neural network) integrated with a CBAM (convolutional block attention module) and a LSTM (long short-term memory network). By incorporating attention mechanisms, both channel and spatial features were effectively extracted within the CNN framework. Additionally, LSTM was introduced to enhance the extraction of temporal features, enabling accurate recognition of SSVEP signals. The experimental results show that the proposed method can effectively extract hierarchical features and achieves a high recognition accuracy.Compared to canonical correlation analysis (CCA), CNN, CBAM-LSTM, and CNN-CBAM, the proposed model improves the recognition accuracy by 5.3%, 2.95%, 2.27%, and 1.71% respectively. It can be seen that the model has a good performance in the classification and recognition of SSVEP signals.

With the rapid development of the Internet and social platforms, the problem of spammer detection has become a major technical challenge in building a harmonious Internet environment. However, user data collected from social platforms are often subject to issues such as missing information and data noise. Therefore, in graph-based learning models for bot army detection, methods that use point estimation as weights fail to express uncertainty in regions with sparse or missing data. A graph neural network model for bot army detection, VRGAT, integrating variational inference, was proposed. It introduces a probability distribution for the weights and derives a variational approximation of the true posterior. By applying different convolution operations to the mean and variance, the model more accurately captures the variability in the data. Simulations based on the Twibot-20 dataset show that, compared to the best existing benchmark for bot army detection (F₁ = 88.12), VRGAT achieved an improved performance with an F₁ score of 89.64.In robustness experiments, when random noise was added at varying levels, the accuracy drop for VRGAT is significantly slower than for other baseline models, demonstrating its superior noise resistance. The experimental results demonstrate that the introduction of variational inference can enhance the effectiveness of spammer detection and improve the model's robustness against noise.

In order to meet the automatic safety needs of the assembly of fixed threaded hole bolts for underground coal mine roadway pipelines, a flexible operation control algorithm for bolt tightening based on admittance method was studied for the six-degree-of-freedom robotic arm equipped with six-dimensional force/torque sensor. The main contents include the theoretical research of gravity compensation algorithm based on six-point positioning method and admittance-based bolt tightening compliance control algorithm. A bolt fastening experimental system based on the robotic arm was set up, and the assembly experiment of fixed threaded hole bolts was carried out. The results indicate a decreasing trend in the forces and torques experienced during assembly, demonstrating the effectiveness and safety of the compliant operation control algorithm based on the admittance method.

Understanding the evolution law of rail service performance is of great significance for reducing the operation and maintenance costs of heavy-duty railway rails. Due to the complex and variable operating environment of rail tracks, which makes it difficult to construct scientifically effective damage evolution indicators to reflect objective development patterns, a method based on t-SNE(t-distributed stochastic neighbor embedding) was proposed for constructing the evolution law of corrugation damage. Firstly, the time-domain, frequency-domain, statistical, and entropy features were extracted from the original rail corrugation vibration signal. The random forest algorithm was then used to rank the features by importance, and the top-ranked features were selected to construct the feature vector. Dimensionality reduction was performed using t-SNE and other methods, and it is found that t-SNE demonstrates superior performance. The final temporal damage degradation index is obtained through Euclidean distance metric and median filtering for smoothing. The results indicate that this method provides good discrimination, anti-interference capability, and practical applicability for damage stages classification.

In the context of unmanned multi-vehicle formation guided by manned vehicles, a system for vehicle recognition and trajectory tracking control of unmanned vehicles during formation driving was devised and executed. An algorithm for multi-sensor fusion moving target detection was proposed, leveraging data from lidar, camera, and mmWave radar sensors. The algorithm utilizes Euclidean clustering, deep learning, and kinematic reasoning techniques for target detection. Additionally, a fusion methodology was introduced to integrate detection outcomes from various sources for precise identification of vehicles in the vicinity. Paths were anticipated based on the trajectories of preceding vehicles, and a Kalman filter was developed to smooth and filter these paths. A vehicle dynamic model, vehicle road error model, and the robust H∞ controller was established for vehicle trajectory tracking control simulation. Outcomes from simulation and real vehicle validation show as follows. The average recognition accuracy of preceding vehicles in test scenarios exceeds 95%. The mean squared error and average trajectory deviation rate of real-time anticipated paths decrease by 17.3% and 48.6% respectively pre and post filtering. Lateral control position error and yaw angle error decrease by 29% and 41% correspondingly compared to PID control. Vehicle formations attain stable working at speeds of up to 54 km/h.

In order to solve the problems of SHO (seahorse optimization), such as low accuracy, precocity and insufficient global search ability. MSHO (multi-strategy seahorse optimization) algorithm based on nonlinear inertial weight strategy, improved whale encircling strategy and improved sine and cosine strategy was MSHO designed. Firstly, the nonlinear inertia weight was introduced into the motion behavior of SHO algorithm to overcome the shortcoming that the algorithm is prone to premature convergence. Secondly, the improved strategy of whale encircling prey was introduced into the updated equation of seahorse hunting success to reduce the probability of the algorithm falling into the local optimal solution. Then, the improved sine-cosine strategy was introduced into the reproduction behavior of the algorithm to enhance the quality of the hippocampal progeny solution, and further improve the global optimization ability and stability of the algorithm. Finally, in order to evaluate the performance of the proposed MSHO algorithm, SHO algorithm, chaotic SHO algorithm, subtraction average algorithm, gray Wolf algorithm, Seagull algorithm, whale optimization algorithm, particle swarm algorithm and MSHO algorithm were compared on 23 benchmark test functions. The experimental results show that MSHO algorithm shows higher convergence accuracy on 20 functions and stronger stability on 16 functions compared with other 7 algorithms. In addition, in order to test the application ability of MSHO algorithm in engineering problems, the algorithm is applied to solve the design problems of welded beams, cantilever beams and pressure vessels. The experimental results show that MSHO algorithm has better search accuracy in these three kinds of engineering design problems than other 7 different algorithms.

An improved version of the EfficientNetV2 network is presented for garbage image classification to address the limitations of mainstream algorithms, such as poor dataset universality, limited recognition types, and algorithmic constraints in specific environments. The proposed algorithm emphasized both classification speed and accuracy. The EfficientNetV2 network was utilized as the baseline model, and classification speed was enhanced through the incorporation of the SK (selective kernel) attention mechanism. Transfer learning strategies were employed to improve classification accuracy. By leveraging deep learning model frameworks for garbage image processing, the need for manual feature extraction from dataset images was eliminated, and the scope of garbage recognition was expanded. Experimental results demonstrate that the proposed algorithm achieves an accuracy of 99.71% on a self-built dataset, which is an improvement of at least 4.77% compared to other algorithms, such as GoogleNet. Furthermore, in terms of time efficiency, the proposed algorithm outperforms algorithms like VggNet19 by at least 50%. Through the enhancement of the EfficientNetV2 network, accurate and faster garbage classification is enabled, providing a scientific and efficient solution to the growing challenges posed by garbage issues.

A semantic segmentation-based method for defect segmentation on thin strip cast and rolled steel plates was proposed to accurately and quickly identify surface defects. Firstly, defect images from the production line were annotated using Labeling software to create a defect segmentation dataset. Secondly, a TransUNet network model was established to recognize and segment surface defects, integrating an optimized DANet dual-attention fusion network to enhance model segmentation performance. Finally, comparative experiments between the improved model and other segmentation models were designed. The feasibility and effectiveness of the proposed method are verified through analysis of experimental results and evaluation metrics. The experiments demonstrate that the improved network achieves a segmentation accuracy of 96.85%, an average intersection over union of 96.99%, and a similarity coefficient of 92.98% for foreign object defects on thin strip cast and rolled steel plates, respectively increasing by 1.19%, 0.61%, and 0.63% compared to the TransUNet network. Additionally, the improved network achieves a segmentation accuracy of 92.86% on the publicly available hot-rolled strip steel defect dataset, indicating its versatility and providing technical guidance for intelligent detection of surface defects on steel plates.

Aiming at the poor performance of existing algorithms in solving large-scale ship path planning problems and the lack of consideration of marine environmental factors such as eddies, a ship path planning method based on punishment pheromone ant colony optimization was proposed. Firstly, three evaluation functions were designed for the planned path: length, risk and heading. Secondly, ACO(ant colony optimization) algorithm inspired by reinforcement learning was designed to search the optimal path, which adds punishment pheromone to the traditional guidance pheromone, which can prevent ants from conducting ineffective searches. Finally, the simulation experiments of the improved algorithm under static environments demonstrate that the proposed algorithm is superior to traditional ACO, jump point search algorithm, and bi-directional search improved ACO in terms of path length, risk value and turn accumulation angle. Compared to the best metrics among these three algorithms, proposed algorithm still achieves a significant improvement in path length reduction of 6.1%, risk value reduction of 5.6%, heading accumulation angle reduction of 78.6%, and iteration number reduction of 53.3%. Especially when the mesoscale eddies and water flow are introduced, the proposed algorithm can still plan a more suitable path for ship navigation, which has positive application significance.

The evaluation of grouting defects in precast beam grouting sleeves has long been a focal point of scholarly research, with the impact of horizontal defects on performance surpassing that of end and vertical defects. Aiming at the problem of horizontal grouting defect evaluation, the active excitation signal of AST (auto sensor test) and PLB (pencil-lead breakage) function built in acoustic emission instrument was studied, combined with horizontal sleeve grouting fullness test.Through parameter analysis and fast Fourier transform, changes in acoustic emission wave velocity, energy, count and waveform parameters are examined at filling degrees of 50%, 60%, 70%, 80%, 90% and 100%. Furthermore, the correlation between acoustic emission energy, count parameters and main frequency amplitude with grouting fullness was established. The results show that when the excitation source is AST, the acoustic emission energy, count and the main frequency amplitude of its waveform show a good negative exponential correlation with the increase of grouting sleeve fullness, and the combination of the relational equations allows for quantitative acoustic emission analysis and evaluation of grouting fullness of horizontal grouting sleeves.

In order to improve the efficiency of finite element analysis and reduce memory consumption, the storage algorithm of the overall matrix was studied. The constraints were uniformly processed as matrix partitions at the element level. Subsequently, taking the stiffness matrix as an example, the distribution pattern of non-zero elements under the constrained condition was determined. A calculation method and corresponding formulas suitable for the number of non-zero elements in the stiffness matrix of two-dimensional and three-dimensional finite elements were proposed, and the correctness was verified. The correspondence between one-dimensional equal bandwidth storage and the original square matrix address was deduced, and it was applied as an auxiliary array in the process of integrating the overall matrix in CSC format, which has improved the efficiency of the overall matrix assembly and avoided the difficulty of node numbering optimization. Finally, the correctness and practicability of the proposed algorithm were verified by using thefour-span single-line girder bridge in the Changdao section of the Fangshan Line in Beijing. The results show that the overall matrix assembly time is reduced by 30%, the storage space is saved by more than 68%, and the calculation efficiency of linear equations is improved by more than 71% while ensuring the calculation accuracy.

In order to accurately select the Copula function to simulate the mutual correlation between inclination and dip of jointed rock mass structural plane, the Copula function method to simulate the occurrence of jointed rock mass structural plane under different fitting indexes was proposed. The optimal Copula function was determined by using the least square Euclidian, AIC information criterion and BIC information criterion, and the optimal edge distribution type of the observed occurrence data of the structural plane was determined by Matlab software. At the same time, Monte Carlo sampling method was used to automatically generate simulation data, and the data was imported into Dips software for visualization processing, and the erP projection map of occurrence was obtained. The difference between the measured dip and inclination data and the simulated data determined by Copula function under different fitting indexes was compared. Finally, the validity of the method is tested based on engineering cases. The results show that different fitting indicators will produce different Copula functions, and there will be great differences in the effectiveness of simulation occurrence. Improper fitting indicators may lead to the selection of inaccurate Copula functions, so that the model can not accurately capture the relevant structure and features of the data. Inappropriate fitting indexes may lead to large errors between the fitting model and the real data, which will decrease the predictive ability and interpretation ability of the model. In this case, it is shown that the Gaussian Copula function selected under the fitting index of least square Euclidene values has the best fitting effect on the measured data. This research will help to select the appropriate fitting index when using Coupla function.

Deformation law is of significant for ensuring the safety of structures and environment during the large diameter shield tunnelling. Particularly for shallow and large diameter shield tunnelling in soft soil, the mutual coupling of high compressibility and low shear strength of soft soil and low overburden load under shallow burial conditions as well as the unloading effect of large excavation will lead to a more complex construction deformation mechanism. Hence, the characteristics of structure vertical deformation, structure convergence deformation and surface deformation caused by shallow and large diameter shield tunnelling in soft soil and their mechanism were studied, based on the dynamic deformation monitoring of a shallow buried large-diameter shield tunnel of Shanghai Suburban Railway Airport Link Line. The results show that the ephemeral demarcation times for different deformation types were closely related. The vertical deformation of the structure can be divided into 4 stages: prior uplift, fluctuating uplift, continuous uplift, and relative stability. The structure convergence deformation can be divided into 3 stages: fluctuating convergence, continuous convergence, and stable convergence. And there are 3 stages, perturbation settlement, rapid consolidation, and relative stability in the vertical deformation of the axial surface. There are significant differences in the vertical structure deformation caused by uneven distribution of additional pressure on the tunnel structure. The maximum vertical surface deformation in the tunnel axis would exceed the warning value and the final settlement deformation would be approximately 84.3% the maximum deformation. The width of the surface sinkhole and location of the maximum settlement are closely related to the depth-to-diameter ratio, but the “swell ridge” distribution would be evident when the depth-to-diameter ratio is relatively large. In about 21~50 d after the excavation construction, the amplitude of structure vertical deformation, structure convergence deformation and surface vertical deformation is significant, and the deformation trend is relatively explicit, which is the key stage to improve the deformation control and remediation effect. The findings of this study are intended to serve as a significant reference for the control of structure deformation and environmental safety protection of shallow and large diameter shield tunnelling in soft soil.

The construction decision of special geological area is very important and complicated. The uncertainty of geological conditions will directly affect the selection and implementation of construction scheme. In order to solve the non-equilibrium problem among the “five control” objectives (time limit, cost, quality, safety and environmental protection) of the bridge construction scheme in the complex special geological area of Southwest China, the network planning technology and BIM(building information modeling) were combined. BIM visualization technology, fuzzy set theory and GRA(grey correlation analysis) were integrated into the optimization of bridge construction schemes in complex special geological areas. HFMD(hybrid fuzzy multi-attribute decision-making model) based on duration-cost-quality-safety-environmental protection was established, and a visualization system of construction process was constructed by using Python and BIM technology. Assisted managers to make decisions, and the result met the “five control” index comprehensive optimization scheme, and the construction period was advanced by 10 days, and the cost was reduced by 3.1%, which proved the practicability and effectiveness of this model A and method. It provides a reference for the decision of bridge construction scheme in complex special geological area.

Cone-shaped hollow foundation is a new form of onshore wind power foundation, which has the advantages of low steel and concrete consumption and reduced waste soil generation during excavation of the foundation pit, compared with the traditional gravity foundation. In order to investigate the bearing characteristics of a cone-shaped hollow foundation under combined loads, a numerical simulation was carried out to investigate the influence of the ratio of the diameter of the top plate to the diameter of the bottom plate on the combined bearing capacity and damage mode of the foundation when the volume of the foundation is held constant. The results demonstrate that when the ratio of the diameter of the top plate to the diameter of the base plate is 6, the vertical ultimate bearing capacity of the cone-shaped hollow foundation is 100% higher than that of the same volume gravity circular foundation, and the moment ultimate bearing capacity is 60% higher. The failure envelopes of different diameter ratios of the cone-shaped hollow foundation can be described by a curve under different combination loads, and the expressions of the failure envelopes of different combination loads are proposed. The failure envelopes of gravity circular and cone-shaped hollow foundations under combined horizontal and bending moment loads show obvious asymmetry, with opposite directions of eccentricity. When the horizontal and bending moment loads are in different directions, the circular foundation has higher bearing capacity. When the horizontal and bending moment are in the same direction, the cone-shaped hollow foundation has higher bearing capacity. The cone-shaped hollow foundation shows a better combination of load-bearing capacity, which can be provided for the super-large single installed capacity wind turbine.

In order to improve the engineering quality problems such as pavement cracking caused by excessive subgrade deformation caused by road use of collapsible loess, alkali activated sustainable material industrial solid waste GGBS(ground granulated blastfurnace slag was used to reinforce and improve the collapsible loess. The influence of different dosage of curing agent on the basic physical properties, mechanical characteristics, permeability and collapsibility of the solidified loess was discussed, and the improvement mechanism of the curing agent was expounded from the microstructure. The results show that the liquid plastic limit of solidified soil increases and the plasticity index decreases. The optimum moisture content decreases first and then increases with the increase of the content of curing agent. The corresponding maximum dry density increases first and then decreases. The maximum dry density of 10% of the content is 1.80 g/cm³. The strength of loess is improved by the curing agent. The strength increases linearly with the increase of the content. The content of 20% curing agent can increase to 2.3 MPa, while the C_BR value of 6% curing agent can increase to 8.3%. The permeability coefficient decreases with the increase of the content of the curing agent. When the content of the curing agent is 10%, the permeability coefficient can be reduced to below 10^-7 m/s, and the collapsibility coefficient decreases with the increase of the content of the curing agent. When the content of the curing agent reaches 6%, the solidified soil becomes non collapsible soil. The comprehensive performance shows that 10% of the content is the optimal amount for road use. In terms of microscopic morphology, the curing agent has changed the contact mode of loess particles. The loess particles have changed from point-surface contact to surface-surface contact. The pore size has changed from middle pore and macropore to middle pore, and the number has decreased. Macropores are filled. Compared with remolded loess, the macropores and mesopores in 10% stabilized soil have decreased from 33.0% and 31.5% to 3.9% and 14.8%, respectively, 29.1% and 16.7%, The fractal dimension of pore distribution decreases from 1.12 to 0.96. Through alkali activated GGBS to solidify collapsible loess, the collapsibility of loess is improved, and its performance has a good road use prospect. This study can provide theoretical basis and practical reference for the consolidation of collapsible loess.

In order to study the hydrothermal characteristics of the sand soil replacement subgrade, the method of indoor freeze-thaw cycle test was used to investigate the hydrothermal changes at different depths of the sand samples with different gradations, and to compare and analyze the effects of coarse sand and fine sand on the silty clay in the lower layer. The results show that during the freezing process, the soil close to the cold end will have a “water increase phenomenon”, and the silty clay is more prone to water migration than the sandy soil. Both the replacement of fine sand and the replacement of coarse sand can inhibit the cooling of the lower silty clay and reduce the freezing depth, so as to avoid the frost heave and thawing of the subgrade. Compared with coarse sand, the replacement of fine sand has a better effect on inhibiting the downward transmission of negative temperature at the top, and the replacement of fine sand is more conducive to maintaining temperature stability. The freezing depth of the replacement of fine sand is smaller than that of the replacement of coarse sand, which is more conducive to inhibiting the frost heave of the subgrade soil and maintaining the internal water stability. The freezing characteristic curve of soil and the thawing characteristic curve of soil do not coincide, and the freeze-thaw characteristic curve of replacement fine sand is least affected by temperature at the interface of replacement. It is concluded that the replacement of sand can effectively avoid the occurrence of frost heave phenomenon of subgrade, and the effect of replacing fine sand is better than that of coarse sand.

In order to ensure the safety and reliability of the structural connection of assembled bridges, the strength test design of different types of interface agents was carried out based on bridge engineering and structural mechanics, and the mechanical properties of different cement grades, water-cement ratio and ash-sand ratio were analyzed. The change law of the strength of cement mortar in the early stage is faster than that in the later stage. The change law of the compressive strength of different types of interface agents with different ages was obtained, the prediction model of the relationship between different ages and strength of cement mortar was constructed, and the optimal mechanical properties of SS-III were proposed from the perspective of the bending-compression ratio. The relationship between different interface agents and the tensile strength of adhesion splitting was tested and analyzed by developing the test device of adhesion splitting tensile strength. The bonding performance of SS-III was determined to be the best interface bonding agent for assembled bridges from the perspective of the bending ratio and bonding properties. cement mortar as the interface agent for assembled bridges is more reasonable, which provides a new research idea for the safety and reliability analysis of the interface connection of assembled bridges.

In order to solve the problem that the tunnel crosses the water-rich fault fracture zone, the construction risk is large, and the problems of surrounding rock instability and water inrush are very likely to occur. Based on the engineering background of the Xiaocaoba tunnel of the Chongqing-Kunming high-speed railway crossing the water-rich fault fracture zone, the fluid-structure interaction numerical model was established by using FLAC^3D to study whether to consider the influence of groundwater action, different grouting forms, grouting ring thickness and the force of the supporting structure under the influence of groundwater. The results show that the stability of the surrounding rock is poor under the consideration of groundwater, and after the tunnel excavation, the groundwater is distributed in a “funnel-shaped” manner around the tunnel after the seepage reaches a steady state, and the stability of the surrounding rock is enhanced after the advanced grouting reinforcement, and the grouting reinforcement form of the whole perimeter is better than that of the grouting around the arch wall. On the basis of selecting the grouting form, the parameters of the grouting ring were continuously optimized, and it was concluded that with the increase of the ratio of grouting ring thickness and permeability coefficient, it can effectively reduce the displacement of surrounding rock, limit the development of plastic zone, and reduce the pore water pressure of the primary branch, and the increase of the thickness of the grouting ring can significantly change the distribution range of the pore water pressure of the primary branch. After the construction of the appropriate grouting scheme on site, the feasibility of the grouting scheme and the rationality of the selection of grouting parameters were verified by comparing the monitoring values around the tunnel with the simulated values, and the stability of the surrounding rock was effectively controlled. The research results can provide reference value for the design and construction of similar tunnel projects in the future.

To study the influence of dense floor openings on existing main structures of subway stations, based on the reconstruction and expansion project of Dongsi Shitiao Station on Line 3 of Beijing Subway, finite element analysis method was used to simulate the dense openings on the bottom plate and the construction of vertical shafts. The stress and deformation characteristics of the station main structure were analyzed, and the impact of different phased opening schemes on the structural deformation of the station floor was discussed. Finally, verification was conducted in conjunction with on-site monitoring. The results show as follows. During the excavation and support process of shafts after floor opening, staggered excavation of adjacent shafts can effectively reduce the deformation of the station main structure, with the deformation being related to the depths of the two shafts and the distance between them. When the number of floor openings is large and dense, the opening order needs to be reasonably allocated. Through comparison and optimization of different phased opening schemes, it is found that compared to the three-phase opening scheme, the two-phase opening scheme increases the settlement by an average of 0.3 mm. However, the former shortens the construction period, improves construction efficiency, and is therefore recommended under the condition of meeting deformation control requirements.

Traffic accidents pose significant risks to public safety and represent a critical issue in transportation systems. The accurate prediction of accident severity is essential for implementing effective prevention and intervention measures. An ensemble learning approach, combining the advanced algorithms XGBoost and MLP, was proposed to enhance the accuracy of traffic accident severity predictions. A stacked classifier was established and its performance in traffic accident prediction was thoroughly evaluated. The experimental results demonstrate that the integrated model significantly improves prediction accuracy compared to the traditional XGBoost model, with a notable 20.41% increase in the macro-average F₁ score. The advantages and innovations of the model, including model integration and network transformation, were highlighted. Additionally, the key features affecting the prediction results were analyzed, and the model's potential value in practical applications was explored. This study provides more scientific and efficient decision support for traffic safety management and is expected to play a crucial role in fields such as traffic management and intelligent driving.

The precision of temperature field control in the hypersonic wind tunnel directly affects the accuracy of wind tunnel test data. In view of the control problems of hypersonic wind tunnel temperature field control, such as large delay, nonlinear and multi-variable coupling, phase space reconstruction of data affecting temperature was carried out and support vector regression was applied to the hypersonic wind tunnel temperature field predictive control to improve the accuracy and efficiency of hypersonic wind tunnel temperature field control. At the same time, considering that the selection of kernel function in support vector regression machine and the optimization of kernel function parameters affect the accuracy of prediction results, the support vector machine model was established based on different kernel functions, and the optimal kernel function was selected through comparative verification and analysis, and the corresponding PSR-SVR model was established to predict the temperature field of the hypersonic wind tunnel, so as to improve the temperature prediction accuracy. The analysis of actual temperature field data shows the effectiveness of the proposed method.

Unique test requirements in civil large aircraft flight testing, characterized by short task durations, wide measurement point distribution, and numerous measurement locations, are addressed. Challenges in the existing wired Ethernet-based onboard data acquisition systems, including difficult measurement equipment installation, complex test cable layout, and prolonged retrofitting periods, are identified. A flexible, miniaturized, and compact space-compatible measurement system for critical wing structural state parameter measurements during civil aircraft flight testing was proposed. An integrated microsystem, including a flexible antenna module and a multi-sensor parameter collection module, was developed and integrated into the civil aircraft wing. The system's reliability and stable signal transmission were demonstrated. The design and application of a wireless flexible measurement system for wing state monitoring on civil aircraft were detailed. The system design approach, data transmission strategy, and integration with third-party loggers were described. Ground and flight tests were conducted to collect data.The onboard flexible system's capability to measure temperature, three-axis vibration, and pressure is verified.

Antibiotics are new pollutants in water environment, which have potential threat to aquatic ecological environment and human health. Considering that sulfanilamide antibiotics are difficult to degrade and oxidize and cannot be effectively removed by sewage treatment plants, Fe₇₈Si₉B₁₃/H₂O₂ system was constructed to efficiently and environmentally degrade sulfanilamide antibiotic pollution. Fe₇₈Si₉B₁₃/H₂O₂ system was selected as the best process for efficient degradation of SDZ(sulfadiazine) through a series of experiments with different experimental parameters. The experimental results show that 250 μg/L SDZ can be degraded 100% within 4 min at pH=2, H₂O₂=50 μmol/L and temperature 25 ℃, and its apparent rate constant (K_obs) can reach 0.99 min^-1. The activation energy of SDZ removal in the system is 25.42 kJ/mol. The quenching agent experiments show that the key active substance of Fe₇₈Si₉B₁₃/H₂O₂ system degrades SDZ is hydroxyl radical (·OH). Through high resolution identification of intermediates and toxicity assessment of the intermediates, the results show that all intermediates are substantially reduced or harmless compared to the mother. In addition, the practical application potential of the oxidation degradation system is verified through the actual water degradation experiment.

The resilience theory has been introduced into the aviation logistics supply chain to enhance its ability to withstand internal and external shocks and uncertainty. A resilience evaluation model for the aviation logistics supply chain based on a Bayesian network was proposed. The Bayesian network structure index was established from the macro performance layer, the middle influence layer, and the bottom cause layer. Subsequently, the Bayesian network model was constructed, and the probability inference process of the Bayesian network nodes was completed using the Markov Chain Monte Carlo algorithm. This enabled the acquisition of the probability distribution of aviation logistics supply chain resilience through parameter learning and model inference. Furthermore, the Bayesian network model was employed for reverse reasoning, sensitivity analysis, and cause chain analysis to identify the key variables and sensitive factors affecting the resilience of the aviation supply chain. Based on these analyses, suggestions for improving resilience were put forward. Research findings indicate that the resilience value of China's aviation logistics supply chain is approximately 53%, signifying a high level of resilience. The Bayesian network model has proven effective in quantifying the resilience value of the aviation logistics supply chain, as well as in reasoning and analyzing the influence of each factor on resilience value, thus contributing to the enhancement of the risk resistance ability of the aviation logistics supply chain.